UAV-Based Hyperspectral Imagery for Detection of Root, Butt, and Stem Rot in Norway Spruce
Numerous species of pathogenic wood decay fungi, including members of the genera <i>Heterobasidion</i> and <i>Armillaria</i>, exist in forests in the northern hemisphere. Detection of these fungi through field surveys is often difficult due to a lack of visual symptoms and is...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-08-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/14/15/3830 |
| _version_ | 1827618398916313088 |
|---|---|
| author | Benjamin Allen Michele Dalponte Hans Ole Ørka Erik Næsset Stefano Puliti Rasmus Astrup Terje Gobakken |
| author_facet | Benjamin Allen Michele Dalponte Hans Ole Ørka Erik Næsset Stefano Puliti Rasmus Astrup Terje Gobakken |
| author_sort | Benjamin Allen |
| collection | DOAJ |
| description | Numerous species of pathogenic wood decay fungi, including members of the genera <i>Heterobasidion</i> and <i>Armillaria</i>, exist in forests in the northern hemisphere. Detection of these fungi through field surveys is often difficult due to a lack of visual symptoms and is cost-prohibitive for most applications. Remotely sensed data can offer a lower-cost alternative for collecting information about vegetation health. This study used hyperspectral imagery collected from unmanned aerial vehicles (UAVs) to detect the presence of wood decay in Norway spruce (<i>Picea abies</i> L. Karst) at two sites in Norway. UAV-based sensors were tested as they offer flexibility and potential cost advantages for small landowners. Ground reference data regarding pathogenic wood decay were collected by harvest machine operators and field crews after harvest. Support vector machines were used to classify the presence of root, butt, and stem rot infection. Classification accuracies as high as 76% with a kappa value of 0.24 were obtained with 490-band hyperspectral imagery, while 29-band imagery provided a lower classification accuracy (~60%, kappa = 0.13). |
| first_indexed | 2024-03-09T10:06:00Z |
| format | Article |
| id | doaj.art-f6bc6eab8a8a4d08987f87d1b93d1d0b |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T10:06:00Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-f6bc6eab8a8a4d08987f87d1b93d1d0b2023-12-01T23:08:59ZengMDPI AGRemote Sensing2072-42922022-08-011415383010.3390/rs14153830UAV-Based Hyperspectral Imagery for Detection of Root, Butt, and Stem Rot in Norway SpruceBenjamin Allen0Michele Dalponte1Hans Ole Ørka2Erik Næsset3Stefano Puliti4Rasmus Astrup5Terje Gobakken6Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, 1432 Ås, NorwayResearch and Innovation Centre, Fondazione E. Mach, Via E. Mach 1, 38098 San Michele all’Adige, TN, ItalyFaculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, 1432 Ås, NorwayFaculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, 1432 Ås, NorwayNorwegian Institute for Bioeconomy Research (NIBIO), Division of Forest and Forest Resources, National Forest Inventory, Høgskoleveien 8, 1433 Ås, NorwayNorwegian Institute for Bioeconomy Research (NIBIO), Division of Forest and Forest Resources, National Forest Inventory, Høgskoleveien 8, 1433 Ås, NorwayFaculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, 1432 Ås, NorwayNumerous species of pathogenic wood decay fungi, including members of the genera <i>Heterobasidion</i> and <i>Armillaria</i>, exist in forests in the northern hemisphere. Detection of these fungi through field surveys is often difficult due to a lack of visual symptoms and is cost-prohibitive for most applications. Remotely sensed data can offer a lower-cost alternative for collecting information about vegetation health. This study used hyperspectral imagery collected from unmanned aerial vehicles (UAVs) to detect the presence of wood decay in Norway spruce (<i>Picea abies</i> L. Karst) at two sites in Norway. UAV-based sensors were tested as they offer flexibility and potential cost advantages for small landowners. Ground reference data regarding pathogenic wood decay were collected by harvest machine operators and field crews after harvest. Support vector machines were used to classify the presence of root, butt, and stem rot infection. Classification accuracies as high as 76% with a kappa value of 0.24 were obtained with 490-band hyperspectral imagery, while 29-band imagery provided a lower classification accuracy (~60%, kappa = 0.13).https://www.mdpi.com/2072-4292/14/15/3830hyperspectral imageryUAVrootbutt & stem rot<i>Heterobasidion</i>remote sensing |
| spellingShingle | Benjamin Allen Michele Dalponte Hans Ole Ørka Erik Næsset Stefano Puliti Rasmus Astrup Terje Gobakken UAV-Based Hyperspectral Imagery for Detection of Root, Butt, and Stem Rot in Norway Spruce Remote Sensing hyperspectral imagery UAV root butt & stem rot <i>Heterobasidion</i> remote sensing |
| title | UAV-Based Hyperspectral Imagery for Detection of Root, Butt, and Stem Rot in Norway Spruce |
| title_full | UAV-Based Hyperspectral Imagery for Detection of Root, Butt, and Stem Rot in Norway Spruce |
| title_fullStr | UAV-Based Hyperspectral Imagery for Detection of Root, Butt, and Stem Rot in Norway Spruce |
| title_full_unstemmed | UAV-Based Hyperspectral Imagery for Detection of Root, Butt, and Stem Rot in Norway Spruce |
| title_short | UAV-Based Hyperspectral Imagery for Detection of Root, Butt, and Stem Rot in Norway Spruce |
| title_sort | uav based hyperspectral imagery for detection of root butt and stem rot in norway spruce |
| topic | hyperspectral imagery UAV root butt & stem rot <i>Heterobasidion</i> remote sensing |
| url | https://www.mdpi.com/2072-4292/14/15/3830 |
| work_keys_str_mv | AT benjaminallen uavbasedhyperspectralimageryfordetectionofrootbuttandstemrotinnorwayspruce AT micheledalponte uavbasedhyperspectralimageryfordetectionofrootbuttandstemrotinnorwayspruce AT hansoleørka uavbasedhyperspectralimageryfordetectionofrootbuttandstemrotinnorwayspruce AT eriknæsset uavbasedhyperspectralimageryfordetectionofrootbuttandstemrotinnorwayspruce AT stefanopuliti uavbasedhyperspectralimageryfordetectionofrootbuttandstemrotinnorwayspruce AT rasmusastrup uavbasedhyperspectralimageryfordetectionofrootbuttandstemrotinnorwayspruce AT terjegobakken uavbasedhyperspectralimageryfordetectionofrootbuttandstemrotinnorwayspruce |